Target Tracking Using Wireless Sensor Networks

Tracking of targets in remote inaccessible areas is an important application of Wireless Sensor Networks (WSNs). The use of wired networks for detecting and tracking of intruders is not feasible in hard-to-reach areas. An alternate approach is the use of WSNs to detect and track targets. Furthermore, the requirements of the tracking problem may not necessarily be known at the time of deployment. However, issues such as low onboard power, lack of established network topology, and the inability to handle node failures have limited the use of WSNs in these applications. In this dissertation, the performance of WSNs in remote surveillance type of applications will be addressed through the development of distributed tracking algorithms. The algorithm will focus on identifying a minimal set of nodes to detect and track targets, estimating target location in the presence of measurement noise and uncertainty, and improving the performance of the WSN through distributed learning.The selection of a set of sensor nodes to detect and track a target is first studied. Inactive nodes are forced into `sleeping' mode to conserve power, and activated only when required to sense the target. The relative distance and angle of the target from sensor nodes are used to determine which of the sensors are needed to track the target.The effect of noisy measurements on the estimation of the position of the target is addressed through the implementation of a Kalman filter. Contrary to centralized Kalman filter implementations reported in the literature, implementation of the distributed Kalman filter is considered in the proposed solution.Distributed learning is implemented by passing on the knowledge of the target, i.e. the filter state and covariance matrix onto the subsequent node running the filter. The problem is mathematically formulated, and the stability and tracking error of the proposed strategy are rigorously examined. Numerical examples are then used to demonstrate the utility of the proposed technique.It will be shown by mathematical proofs and numerical simulation in this dissertation that distributed detection and tracking using a limited number of nodes can result in efficient tracking in the presence of measurement noise. Furthermore, minimizing the number of active sensors will reduce communication overhead and power consumption in networks, improve tracking efficiency, and increase the useful life span of WSNs

Simulation optimisation to inform economic evaluations of sequential therapies for chronic conditions: a case study in Rheumatoid Arthritis

This thesis investigates the problem of treatment sequencing within health economic evaluations. For some chronic conditions, sequences of treatments can be used. When there are a lot of alternative treatments, then the number of possible sequences becomes very large. When undertaking an economic evaluation, it may not be feasible to estimate the costs and benefits of every alternative treatment sequence. The objective of the thesis is to test the feasibility of simulation optimisation methods to find an optimal or set of near-optimal sequences of disease modifying treatments for rheumatoid arthritis in an economic evaluation framework. A large number of economic evaluations have been undertaken to estimate the costs and benefits associated with different treatments for rheumatoid arthritis. Many of these have not considered the downstream sequence of treatments provided, and no published study has considered identifying the best, or optimal, treatment sequence. The published evidence is therefore of limited applicability if the objective is to maximise patient benefit while constrained by a finite budget. It is plausible that decision-makers have developed sub-optimal guidance for rheumatoid arthritis, and this could extend to other chronic conditions. A simulation model can provide an expectation of the population mean costs and benefits for alternative treatment sequences. These models are routinely used to inform health economic evaluations. However, they can be computationally expensive to run, and therefore the evaluation of potentially millions of treatment sequences is not feasible. However, simulation optimisation methods exist to identify a good solution from a simulation model within a feasible period of time. Using these methods within an economic evaluation of treatment sequences has not previously been investigated. In this thesis I highlight the importance of the treatment sequencing problem, review and assess relevant simulation optimisation methods, and implement a simulated annealing algorithm to explore its feasibility and appropriateness. From the implementation case study within rheumatoid arthritis, simulation optimisation via simulated annealing appears to be a feasible method to identify a set of good treatment sequences. However, the method requires a significant amount of time to implement and execute, which may limit its appropriateness for health resource allocation decision making. Further research is required to investigate the generalisability of the method, and further consideration regarding its use in a decision-making context is important